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2AM shows that task memory can remain Agent-side and shows that Action Model capability depends not only on what the policy has learned, but on how precisely the Agent can steer it, and isolates this question through a deliberately constrained design.
Object-object interactions can dramatically reduce the training data needed for effective manipulation policies, achieving superior performance with less complexity.
Assistron achieves a remarkable balance between autonomy and user control, significantly enhancing task success while reducing user effort in daily activities.
FF-JEPA transforms long-horizon planning by enabling goal-free trajectory optimization without the need for explicit goal images.